Eur J Cardiothorac Surg 2007;32:644-647. doi:10.1016/j.ejcts.2007.06.042
Copyright © 2007, European Association for Cardio-Thoracic Surgery. Published by Elsevier B.V. All rights reserved
Emergency treatment of chest trauma — an e-learning simulation model for undergraduate medical students
Josef Smollea,*,
Gerhard Prauseb,
Freyja-Maria Smolle-Jüttnerc
a Medical University of Graz, Institute of Medical Informatics, Statistics and Documentation, Graz, Austria
b Medical University of Graz, Department of Anaesthesiology, Graz, Austria
c Medical University of Graz, Department of Thoracic and Hyperbaric Surgery, Graz, Austria
Received 17 April 2007;
received in revised form 26 June 2007;
accepted 28 June 2007.
* Corresponding author. Address: Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Billrothgasse 18a/7, A-8010 Graz, Austria. Tel.: +43 316 385 72052; fax: +43 316 385 72059. (Email: josef.smolle{at}meduni-graz.at).
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Abstract
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Objective: Appropriate emergency measures are essential in improving the outcome of patients with thoracic injuries. Pathophysiological background and basic principles of emergency treatment decisions should be already taught in undergraduate medical curricula. The effectiveness of a computer simulation model on thoracic trauma management was evaluated. Methods: Forty-one students were enrolled in this pre-test/post-test self-controlled study. Learning experience was based on a complex computer simulation model demonstrating basic mechanisms of thoracic injuries and facilitating the interactive application of various emergency measures. Results: Pre-test multiple-choice results were 72.2% (66.9–77.5) correct answers, which increased significantly to 86.5% (82.6–90.4) in the post-test (p
< 0.001). The students spent 30 min (23–36) with the interactive learning object. Content analysis of open-ended feedback revealed a highly significant overall positive judgement (p
< 0.001), where the importance of ‘trial and error’ learning, the possibility of being able to ‘view a process’ and the simplicity of the model were particularly stressed. Conclusions: Computer simulation of chest trauma emergency treatment options is a safe and efficient learning approach in undergraduate medical education, which is highly appreciated by the students.
Key Words: Education • Learning aids • Trauma, blunt • Trauma, penetrating • Pneumothorax
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1. Introduction
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Trauma to the thorax with injury of the chest wall and/or thoracic organs is encountered in 50–60% of all cases with polytrauma. Besides head injuries, thoracic injuries are the most common cause of fatal outcome in these patients [1,2]. Immediate and appropriate intervention is essential in improving prognosis [1,2]. Since thoracic surgery specialists will be available only in a minority of accidents, basic education in chest trauma management should be provided to all physicians who might be involved in emergency treatment. A fundamental understanding of the underlying pathophysiological mechanisms and the rational choice of appropriate measures should be delivered already during undergraduate medical education.
Computer simulation models are particularly suitable for demonstrating complex processes and functional relationships. Furthermore, simulation models facilitate interactive training of medical skills without putting patients at risk in service of education [3]. Therefore, we created a simulation model demonstrating the consequences of various chest trauma patterns and the effects of standard treatment modalities. This simulation model is part of the undergraduate medical curriculum of the Medical University of Graz and uses the e-learning system VMC Graz (Virtual Medical Campus Graz) [4,5]. In the present study we evaluated the effectiveness of the simulation model in undergraduate medical students.
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2. Students and methods
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2.1 Students
Forty-one students (29 females, 12 males) participated in the study. The procedure had been approved by the institutional review board and all participants gave informed consent. The students had finished their first year of human medicine and were at the beginning of their clinical years. Since e-learning is an integral part of medical education at the Medical University of Graz, all students already had some experience with e-learning.
2.2 Simulation model
The simulation model consisted of a schematic drawing of the upper airways and the main bronchi, the lung, the chest wall, the heart and the diaphragm. Fig. 1
shows a screenshot of the simulation model. Five patterns of injury were simulated: perforation of the thoracic wall without valve effect, perforation of the thoracic wall with valve effect, air leakage of the lung surface, air leakage of the central tracheobronchial system, and combined perforating injury of the thoracic wall and of the lung surface. There were four emergency treatment options available: intubation with positive airway pressure, chest drainage without suction, chest drainage with continuous suction and occlusive taping of the chest wall injury. The model is animated and shows the excursion of the thoracic wall, the diaphragm, the lung, the heart, and eventually of the ventilator. To enhance pathophysiologic understanding, the respective pressure within the pleural cavity and, in case of artificial ventilation, the inspiratory pressure is demonstrated.
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Fig. 1. Chest trauma emergency treatment. Simulation screenshot of basic thoracic physiology, patterns of injury and emergency treatment option.
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The functionality of the model can be accessed in four different modes. In the first mode, the physiologic conditions of normal respiration as well as the effect of removal of the thoracic wall and of positive pressure respirator treatment is demonstrated. In the second mode, one can examine the four treatment modalities. In the third mode, the injury patterns can be examined and the effects of various treatment modalities are studied. In the fourth mode, injuries are randomly presented and the student is encouraged to choose the appropriate treatment. In each situation, the system reacts according to the type of injury and the chosen treatment modalities. For example, choosing intubation and positive pressure ventilation without chest drainage in presence of a pulmonary air-leak will immediately show the result of tension pneumothorax with mediastinal shift.
2.3 Study design
The study consisted of four phases:
- Phase 1: Pre-test. Pre-simulation multiple choice (MC) test on chest trauma emergency management. Nine MC questions had been randomly selected from a pool of 18 MC questions for the pre-simulation test. The remaining nine questions were used for the post-simulation test. The percentage of correct answers was recorded. There was no specific introduction on chest trauma prior to the pre-test.
- Phase 2: Simulation. The students did their training using the computer simulation. Along with the learning object, they received detailed instructions on how to handle the simulation. They were encouraged to follow the instructions, but there was no supervision as to what degree they really did. The time each student spent on the simulation was recorded.
- Phase 3: Post-simulation MC test. Each student performed a test of nine randomly selected MC questions on the topic of chest trauma emergency treatment. The questions were different from those of the pre-simulation MC test. The percentage of correct answers was recorded.
- Phase 4: Each student was encouraged to give full-text, open-ended feedback concerning the overall value of the simulation, the personal learning experience and the potential utility of the instruction. The open-ended feedback was explored by manual content analysis.
The primary end point was the difference in the MC test results before and after the students had undergone the simulation. Additional outcome measures were the time spent with the simulation, the frequency of various content analysis categories and the statistical relationship of the variables.
Furthermore, utilization of the electronic learning objects in undergraduate teaching was recorded.
2.4 Statistics
Statistical analysis was performed using the SPSS 13.0 statistical software package for social sciences (SPSS Inc., Sunnyvale, USA). Absolute and relative frequency, mean, standard deviation, and 95% confidence interval were used where appropriate. The difference of pre-simulation and post-simulation test results was examined by Student's t-test for paired values. Student's t-test for independent samples, Spearman's rank correlation test, Fisher's exact test and sign test were applied where appropriate. A p value of less than 0.05 was considered to indicate statistical significance.
Power analysis [6] was based on the aim to detect an increase between pre- and post-simulation MC tests of at least 10%. With alpha = 0.05 and beta = 0.1, and a standard deviation of 14%, a minimum number of 17 students would suffice.
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3. Results
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Forty one students participated in this study. There were 29 females and 12 males. They required 30 ± 20 min (95% confidence interval 24–36) min for performing the entire simulation.
The results of the MC tests are summarized in Table 1
. In the pre-simulation test, the students had 72 ± 17% (95% confidence interval 67–78%) correct answers, after performing the simulation they achieved 86 ± 12% (83–90%) correct answers (Fig. 2
). The individual difference was 13 ± 14% (8–17%). This difference was highly significant (t-test for paired samples: t
= 7.05, p
< 0.001). This significant improvement was encountered in the subgroup of females as well as in the subgroup of males (p
< 0.01 and p
< 0.05, respectively). There was no significant gender-specific difference as to the results in the MC tests. There was a highly significant correlation between the pre-simulation and post-simulation results (r
= 0.647, p
< 0.001).
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Fig. 2. Chest trauma emergency treatment. Pre- and post-simulation multiple choice test results (% correct answers).
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The time spent with the simulation did not correlate with the post-simulation test results. There was, however, a significant negative relationship between the time spent with the simulation and the individual difference between the pre- and post-simulation tests (Spearman's rank correlation test: r
= –0.47, p
> 0.01).
Concerning feedback, 83% gave an overall positive voting, in contrast to an overall negative statement in 5% (Table 2
). This is a highly significant result in favour of a positive judgement (sign test: z
= –5.48, p
< 0.001). Twenty percent particularly emphasized the importance of being able to ‘view’ a process, and 17% claimed the usefulness of the simulation to acquire ‘understanding’. Also 17% expressed the importance of the possibility of trial and error, which they considered rather helpful. Ten percent stated that the simulation was strikingly better than a textbook. Fifteen percent emphasized the ‘simplicity’ of the simulation. The design, which placed the comments always into the same window as the simulation, was valued by 7%. There were ambiguous results concerning the importance of the instruction. Nineteen (46%) considered it helpful, while 21 (51%) found it superfluous. 12 students (29%) claimed that the font size of the explanatory text in the simulation was too small and reported difficulties in reading. The two registered overall negative statements referred to e-learning in general (‘no alternative to an attendance class’) and one student noted that the animated movements of the heart and the thoracic wall were ‘bothering’.
There was no relationship between the judgement of the written instruction (positive or negative) and the MC test results (t-test for paired values: p
> 0.05). The only relationship between feedback statements and results was found for the post-simulation MC test results. These were significantly higher (91 ± 12%) for students who considered the written instruction as superfluous, compared to those who found it valuable (82 ± 11%; t-test: p
< 0.05). Those who found the written instruction valuable showed a tendency to spend less time with the simulation than others (24 ± 12 min compared to 36 ± 24 min; t-test: t
= 1.986, p
= 0.056).
The feedback judgements did not correlate with sex except that a negative rating of the instruction was more common in females (62%) than in males (25%; Fisher's exact test: p
< 0.05).
As far as the overall utilization of the simulation approach is concerned, from October 2005 to January 2007, students of the undergraduate medical curriculum paid 2509 visits to the electronic learning unit on chest trauma emergency management.
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4. Discussion
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Our study shows that a computer-based model of thoracic emergency situations illustrating the related pathophysiological and therapeutic effects significantly increases the performance of students in MC tests on the same topic. The overall time spent with the simulation of about half an hour is rather short for studying the subject of thoracic injury, related pathophysiology and emergency treatment measures. The fact that the results of the pre-simulation and post-simulation MC tests showed a highly significant improvement underscores the gain of knowledge associated with the simulation model. The relatively high performance already in the pre-test might be due to the fact that several aspects of chest trauma can be explained by application of pre-clinical knowledge alone. Gain of knowledge was noted similarly in female and male participants and did not seem to favour either of both groups. Therefore, gender-specific differences in computer literacy [7] do not seem to play a role in our study.
It is remarkable that the time spent with the simulation correlated negatively with the difference between the pre- and post-simulation results. Obviously the gain of knowledge did not depend on the time spent with the simulation. Probably high performers are able to deal with the simulation in a more efficient and less time-consuming way.
The overwhelming positive feedback of most students showed a high level of acceptance of the simulation approach. The aspects the students valued most is in good agreement with previous reports: interactivity, the possibility to learn by trial and error, and the advantage of seeing a dynamic process happen [8,9]. The fact that the ‘simplicity’ of the model was mentioned several times is in agreement with a statement of Grunwald, that more sophisticated learning objects are by no means the more effective ones [10]. Hoernlein, in particular, pointed out that reducing the complexity of the user interface increases acceptance [11].
It is interesting that a detailed instruction is valued by about half of the students, while the others consider it superfluous. Obviously some enjoy the possibilities on exploring a model on their own, while others are grateful when being offered a stringent learning path. One may speculate that these two groups correspond to the sequential-versus-global learning styles according to Solomon's index of learning styles [12]. Since those who worked without the instruction performed better in the subsequent test, the model seems to favour active instead of reflective learners.
Utilization statistics from the undergraduate curriculum reveal that the model also has a high acceptance by the students in everyday learning.
There are several drawbacks of the study, which should be taken into account. First, the model is highly schematic and simplified. Second, the number of participants was rather small. Furthermore, the gain in knowledge was only assessed by MC test results. Change of student behaviour in real emergency situations, for example, has not yet been evaluated. Finally, the pre-test itself might be helpful in increasing knowledge on chest trauma, even though different questions were used for pre- and post-test.
In conclusion, a computer simulation model of thoracic injuries and emergency treatment modalities seems to be an effective tool in increasing students’ understanding of the subject. Besides the gain of knowledge, the overall acceptance of the didactic approach is high. This model, or similar ones, may be considered as an additional educational tool in pre- and postgraduate thoracic surgery and emergency medicine education.
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Acknowledgments
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Mrs Pamela Bauer is kindly thanked for data administration. Mr Gottfried Schipfer and Mr Michael Eisner are kindly thanked for programming assistance.
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Footnotes
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\#9734; The work was funded by grants from the Federal Ministry of Education, Science and Culture, Austria.
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References
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Abstract
1. Introduction
